Conventional spark ignition engines may regulate combustion phasing with map-based spark timing control. The calibration of such maps can consume significant amounts of time and resources making it less favorable for spark ignition engines having a high number of control actuators. Spark timing can have a significant influence on fuel efficiency, torque, and emissions. In this manner, accurate calibration of spark timing can be of critical importance to overall system performance. In particular, controlling spark timing to achieve optimal combustion phasing can be beneficial to spark ignition engine performance and efficiency.
Internal residual gases associated with combustion engines are generally composed of exhaust gases that have been recycled from a previous engine cycle. These gases are almost always present in-cylinder due at least in part to mechanical limitations in clearing the entire cylinder volume, pressure driven backflows into the intake, and/or valve overlap strategies. Mass-production sensors for direct measurement of internal residual gas mass (or fraction) are generally not currently available, driving the need for fast and accurate prediction models for the purposes of engine control. Residual gas mass (RGM) prediction is a key enabler for model-based engine control strategies because it is a key input for combustion phasing control, air mass determination, and/or other algorithms.
Semi-empirical residual gas prediction models are a popular consideration for engine control purposes due to their simplified model form and reduced computational efforts. Control-oriented residual gas calculation models have been developed using several different semi-empirical correlations. While some of these models may not have originally been intended for real-time control, their computational complexity is of a level that they have now become feasible for implementation in modern engine controllers. These correlations can utilize either standard engine sensors (e.g. intake manifold pressure, engine speed, etc.) and/or models that are generally available within an engine controller (e.g. exhaust pressure and temperature). Physics-based energy balance residual gas calculation methods have also been developed that can reduce or eliminate calibration constants. These methods may demonstrate high accuracy, but they rely on crank angle resolved calculations that increase on-board processing requirements as compared to semi-empirical approaches.
Conventional semi-empirical approaches may include separating residual gas into two terms: burned gas from backflow into the cylinder during valve overlap and trapped residual gas due to clearance volume. Experimental RGF data can be used to calibrate model constants and the model may be suitable for real-time RGF prediction. This widely used model, however, neglects the influence of dynamic pressure waves in the intake and exhaust. Additionally, the model predicts residual gas fraction, which means that uncertainty in volumetric efficiency can influence the residual mass prediction. Other RGF models have been developed that are based on intake and exhaust manifold pressure ratio, compression ratio, AFR, cylinder intake volumetric efficiency and/or EGR percentage. Such models may not explicitly contain empirical fit constants, but may require a method for volumetric efficiency prediction. Various other models have been developed. However, such models can be inefficient and/or inaccurate.